Hybrid weak-perspective and full-perspective matching

Full perspective mappings between D objects and D images are more complicated than weak perspective mappings which consider only rotation translation and scaling Therefore in D model based robot navigation it is important to understand how and when full perspective must be taken into account In this paper we use a probabilistic combinatorial op timization algorithm to search for an optimal match between D landmark and D image features Three variations are considered a weak perspective algo rithm rotates translates and scales an initial D pro jection of the D landmark A full perspective algo rithm always recomputes the robot s pose and repro jects the landmark when testing alternative matches Finally a hybrid algorithm uses weak perspective to select a most promising alternative but then updates the pose and reprojects the landmark The hybrid al gorithm appears to combine the best attributes of the other two Like the full perspective algorithm it reli ably recovers the true pose of the robot and like the weak perspective algorithm it runs to faster than the full perspective algorithm